Software Engineer – Machine Learning (Search Engine)
Twitter Search is the search engine for Twitter: it’s the place to find the most relevant and engaging content for any topic or interest. We build products on top of a super realtime pipeline that processes nearly one trillion tweets from the whole of Twitter’s history, organizes the world’s conversation as it happens, and personalizes it to each individual user’s needs and context We connect users to the most relevant people and conversations around their interests. We need your help building this exciting product!



Twitter Search is responsible for producing content timelines for keywords, trends, hashtags, topics, realtime events, and even places and emojis. We are not only surfacing tweets, but also users, images, videos, as well as live events. What’s more, we provide features like spelling correction and query suggestions as you type, bringing you even closer to what you need. Twitter Search is also a powerful generic information retrieval system that drives many other products and internal applications at Twitter.



Who We Are

We are a distributed and collaborative team building the real-time Twitter search engine and working across areas such as machine learning, applied data science, recommendation systems, information retrieval systems, natural language processing, large graph analysis, anti-spam and anti-abuse. We put these skills to use finding, personalizing and organizing relevant content for users. We create algorithms by solving a wide range of problems in IR, NLP, and ML with the goal of understanding users’ intent through query and context, ranking and organizing content, and extracting insights to make suggestions for better navigation of content on Twitter.



What You Will Do

You will participate in the engineering life-cycle at Twitter, designing and implementing components, pipelines and algorithms related to machine learning.
Collect, clean up, analyze production and user data to draw insights and produce ideas, working alongside the data scientist in the team.
Collaborating in an engineering team, conducting code reviews and design reviews.
Writing code and tests for production services, offline jobs, and internal tools
Conducting offline and online experiments.
Deploying and maintaining production services, participating in on-call rotations.
Collaborating across teams, working alongside our platform engineers and SREs.


Who You Are

You have a passion for machine learning and improving the ways people communicate and get informed about the world, live. You would like to solve problems in machine learning, information retrieval, text understanding, recommendation, user behavior understanding, and more. You have experience dealing with large data sets in a distributed environment. Also it would be great if you



Have a good grasp of CS fundamentals, data structure, common algorithms.
Comfortable working with at least one OOP or functional language and one interpretive/script language, experience with Java, Scala, and Python a plus.
Have knowledge in one or more of the following fields: machine-learning, information retrieval, recommendation systems, NLP
Have knowledge of distributed systems and parallel computing.
A plus to have experience in collaborating across multiple teams including analytics, product management, and operations.

https://ai-jobs.net/job/software-engineer-machine-learning-search-engine-2/

Learning Symbolic Physics with Graph Networks

Cranmer et al.: https://arxiv.org/abs/1909.05862

#MachineLearning #GraphNetworks #Physics

Chinese Face Recognition vending machine.

No cash, card, or phone needed.

Read: https://www.businessinsider.com/a-tiktok-from-china-shows-facial-recognition-equipped-vending-machine-2019-8

Supervised Machine Learning Lecture Notes - Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön

Download: https://www.it.uu.se/edu/course/homepage/sml/literature/lecture_notes.pdf

Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python, R Programming Resources.: https://www.marktechpost.com/free-resources/

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan : https://arxiv.org/abs/1909.04711
#GenerativeAdversarialNetworks #MachineLearning #Physics

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Yu et al.: https://arxiv.org/abs/1909.05378
#ArtificialIntelligence #Database #MachineLearning

What do Deep Networks Like to Read?
Jonas Pfeiffer, Aishwarya Kamath, Iryna Gurevych, Sebastian Ruder : https://arxiv.org/abs/1909.04547
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Perceptual Image Anomaly Detection. https://arxiv.org/abs/1909.05904


@ArtificialIntelligenceArticles

The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
Elena Voita, Rico Sennrich, Ivan Titov
Blog: https://lena-voita.github.io/posts/emnlp19_evolution.html
Paper: https://arxiv.org/abs/1909.01380
#ArtificialIntelligence #MachineLearning #Transformers

Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning. https://arxiv.org/abs/1909.05477

Evolution of Representations in the Transformer

The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
https://lena-voita.github.io/posts/emnlp19_evolution.html

paper https://arxiv.org/pdf/1909.01380.pdf

Couldn't make it to our Pie & AI meetup in Medellín? Watch the full video of Andrew Ng and Helmuth Trefftz's conversation to learn why we believe in Latin America as a new global AI hub:

https://youtu.be/wlQvPJHxfOE

"A tutorial on energy-based learning"
Yann LeCun, Sumit Chopra, and Raia Hadsell (2006) : https://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf
#EnergyBasedModels #GenerativeModels #GraphTransformerNetworks

DyANE: Dynamics-aware node embedding for temporal networks
Koya Sato, Mizuki Oka, Alain Barrat, Ciro Cattuto : https://arxiv.org/abs/1909.05976
#Physics #Society #MachineLearning #SocialNetworks

1.3 Why study the human brain?
https://www.youtube.com/watch?v=3GK7wDEjrks&fbclid=IwAR0Q7xvmlQ84-u0a52AR2DCpC_kdduWQsRZqp6G88qEZzDf1LYkDzZ4vvvE

Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
Yang Li, José M. F. Moura : https://arxiv.org/abs/1909.04019v3
#MachineLearning #ArtificialIntelligence #Transformer

Help detect serious head injuries by participating in our latest competition, Intracranial Hemorrhage Detection by @RSNA | Read more and Join today!

https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection

Robotics surgeries may currently be an expensive proposition for hospitals, but robots and artificial intelligence will certainly play a major role in the healthcare sector in the future.
Several startups such as DiFacto Robotics, SigTuple and Aindra are working to bring new technologies to reality in India. A group of top executives from hospital chains, investment firms and startups discussed the future of healthcare at the News Corp VCCircle Healthcare Investment Summit, held in Mumbai recently.

https://www.youtube.com/watch?v=1arrmk4XWZE

Full Time Opportunities for PhD Students - AI Development Program

#Microsoft
#Redmond, Washington, United States
https://careers.microsoft.com/us/en/job/660559/Full-Time-Opportunities-for-PhD-Students-AI-Development-Program